According to Mr. Rajiv Ranjan, the Technical Programme Advisor at the PARIS21 Secretariat hosted within the OECD's Statistics and Data Directorate, the ‘Cape Town Global Action Plan (CT-GAP) for Sustainable Development Data’, adopted by the UN Statistical Commission in 2017, identifies “Innovation and modernisation of national statistical systems” as a strategic area. Without innovation management, it is hard for NSOs to take on the data challenges of the 21st century characterised typically by changing data priorities, increasing data demands, burgeoning data supply and competition.

PARIS21 works in over 90 countries including Rwanda and collaborates with a wide-range of institutions globally with the goal of strengthening data ecosystems to deliver quality data for sustainable development. PARIS21 has been involved directly and indirectly, in facilitating thinking and action on innovation at NSOs through several dimensions. For instance, PARIS21 developed a tool called PISTA (Platform for Innovation in Statistics) which serves as a comprehensive repository of innovations in data and statistics worldwide. It allows for easy browsing across different domains and identification of data innovations – both from the production and the use side. Further, PARIS21 has been consistently engaged in the statistical modernisation agenda through technical assistance programmes for NSOs in low and middle-income countries. The PARIS21 Capacity Development 4.0 framework also formalises key ingredients for statistical capacity development in NSOs, relevant for enabling innovation (such as politics, resources, skills/knowledge, incentives and management), something Mr. Ranjan highlighted in his presentation at the conference.

Taking cue from the work of OECD’s Observatory of Public Sector Innovation (OPSI), the expert characterised innovation as “a new or significantly altered process or approach that is novel, that has been implemented in some form, and that is intended to deliver better public outcomes by achieving increased efficiency, effectiveness, and citizen, user or employee satisfaction”.

Advocating for a life-cycle approach as opposed to the linear model of innovation and making it more relevant for NSOs, he proposed to map the innovation process to various stages the Generic Activity Model for Statistical Organisations (GAMSO) – which describes the activities that take place within a typical statistical organization.

In a recorded video, Mr Rajiv Ranjan also talks about some key takeaways from #UNBigData2019

A key strategic area relating to innovation and modernization of national statistical systems (NSSs) includes encouraging NSOs to realise the potential of new and alternative data sources, including big data and embrace the open data initiative, ensuring inclusive stakeholder participation from within and beyond the NSS. However, building an innovation-friendly culture especially in the public sector requires enabling conditions and critical skills especially for public sector innovation. These include iterative processes, data literacy, user-centricity and curiosity (as identified by OPSI). "It is also important to make innovation easier by providing resources, such as appropriate training, data and funding to problems solvers," Rajiv told a group of participants who attended the conference.

One can no longer, see innovation as a specific responsibility of an organisation’s R&D departments, but rather as a core element of the management and culture of the whole organisation. At the heart of innovative agencies lies an operating culture aimed at the rationalisation of production, improvement of service, continuous development of products and processes, and nurturing of innovativeness and creativity among the personnel. Therefore, the innovation agenda is closely associated with the modernization of statistical agencies agenda. Other lessons in the area include the pivotal role of partnerships for knowledge and idea diffusion, the importance of risk-taking, and trial and error for incremental learning.

Making innovation work for official statistics requires sound understanding of its management and basic capacity. This links to the whole debate around statistical capacity development and the need to go beyond pure technical individual skills to systemic enabling conditions. This also reinforces the call for more and better support to statistical agencies worldwide, in the spirit of the CT-GAP for Sustainable Development Data.

The fifth International Conference on Big Data for official statistics was the first of its kind held in Africa and Rwanda. The previous Big Data Conferences took place in October 2014 in Asia (Beijing); in October 2015 in the Middle East (Abu Dhabi); in September 2016 in Europe (Dublin); and in November 2017 in South America (Bogota).